Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy

TL;DR

This paper introduces a zero-shot super-resolution method tailored for endomicroscopy that uses a physically-motivated downsampling kernel and noise modeling, enabling high-quality image enhancement without ground truth data.

Contribution

The authors developed a novel zero-shot super-resolution approach specifically adapted for endomicroscopy, incorporating a physically-motivated downsampling kernel and noise simulation for self-supervised learning.

Findings

ZSSR outperforms baseline methods in image quality.

ZSSR is competitive with supervised super-resolution techniques.

User studies favor ZSSR reconstructions by experts.

Abstract

Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope's irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts.